Can electric vehicle drivers be persuaded to eco-drive? A field study of feedback, gamification and financial rewards in Germany

Energy Research & Social Science 63 (2020) 101407

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Original research article

Can electric vehicle drivers be persuaded to eco-drive? A field study of feedback, gamification and financial rewards in Germany

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Madlen Günthera, , Celina Kacperskib, Josef F. Kremsa ⁎

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Department of Psychology, Chemnitz University of Technology, Wilhelm-Raabe-Str. 43, Chemnitz 09120, Germany Department of Social Psychology, University of Heidelberg, Hauptstraße 47-51, Heidelberg 69117, Germany

ARTICLE INFO

ABSTRACT

Keywords: Persuasive strategy Incentive Eco feedback Energy saving Environmentally friendly driving style Naturalistic driving study Battery electric vehicle

Promoting eco-driving with battery electric vehicles (BEVs) can help drivers reduce their average energy consumption and enhance the driving range of a BEV. The present study examined the influence of three persuasive strategies (feedback regarding energy consumption, gamification, and financial rewards) compared to a baseline condition (no feedback or intervention) on the average BEV energy consumption under natural driving conditions. The influence of persuasive strategies on participants’ attitudes towards eco-driving, as well as self-reported eco-driving knowledge were investigated. The persuasive strategies were used over 22 months. 108 participants took part in the field study in Germany and used BEVs for their daily business travel. Energy consumption data was gathered via data loggers. Participants had unlimited access to their post-drive ecodriving feedback via a web application. The use of game design (i.e. gamification) elements and financial rewards significantly reduced energy consumption as compared to baseline or mere feedback regarding energy consumption. Participants showed a habituation to eco-driving after removal of the strategies. Driving experience with BEV and gamification predicted eco-driving knowledge. We found first evidence from a highly naturalistic field trial by providing driving data from a car sharing case study, improving on previous studies that mostly used laboratory settings or instructed driving. We expanded upon persuasion research, supporting the idea that gamification can be strongly relevant for energy saving behaviour.

1. Introduction Battery electric vehicles (BEVs) are a promising mobility option to meet emission goals in the transport sector [1], which is responsible for about 24% of worldwide CO2-emissions [2]. As long as BEV emissions are still highly dependent on a country's renewable energy ratio [3,4], increasing their energy efficiency, for example by targeting driving behaviour, seems to be a promising approach. Additionally, due to BEVs limited range, energy efficient driving (i.e. eco-driving) minimises the energy consumption of a BEV up to 25% [5] and enhances the driving range. Thus, eco-driving could lower the need for increasing the maximum possible battery capacity, which also negatively affects the ecological footprint of a BEV [6,7]. Eco-driving comprises practices such as driving under the speed limit, avoiding abrupt stops and excessive idling, as well as accelerating only moderately [8] and using regenerative braking rather than hydraulic braking when driving a BEV [9,10]. The efficient usage of ecodriving strategies to reduce consumption has been investigated mostly by providing knowledge-based eco-driving training, such as giving

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participants information about maintaining a constant speed and anticipating traffic [11,12] or in-vehicle feedback devices [13,14], for instance imitating energy efficient driving practices [15]. Studies which have evaluated the post-intervention effectiveness of eco-driving training showed inconsistent effects, which disappeared over time [16,17]. However, investigating ingrained driving patterns, which are developed over years of driving experience, can be costly in terms of time and is not often done, so research is direly needed to investigate effects of longer-term eco-driving programs [13]. Furthermore, investigating eco-driving in private usage settings is highly confounded by the motivation to reduce private costs of electricity or gas. Studies regarding energy conservation behaviour show that employees often feel less responsibility for engaging in energy conserving behaviour [18] and that there are seldom any personal incentives to reduce energy consumption of an organisation [19,20]. Thus, the generalizability of energy conservation behaviour in private settings to organizations is limited [21,22] and achieving commensurable savings in organizations is more difficult compared to households [23]. Thus, it could be expected that the willingness of employees to

Corresponding author. E-mail address: [email protected] (M. Günther).

https://doi.org/10.1016/j.erss.2019.101407 Received 22 May 2019; Received in revised form 6 December 2019; Accepted 11 December 2019 2214-6296/ © 2019 Elsevier Ltd. All rights reserved.

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change their energy saving behaviour, i.e. eco-driving behaviour, is reduced compared to private drivers, and can be researched without the motivation of personal cost savings.

potential of money), there were no differences in participants’ reported intention to adopt eco-driving between both saving potentials [54]. Motives for environmentally friendly driving (e.g. ecological driving motives, cost-benefit orientation) can vary between drivers [55] and could be elicited by the content of feedback [54]. Previous research has indicated that an increasing extrinsic motivation because of financial rewards can ‛crowd out’ previous intrinsic motivation [56]. To our knowledge, studies which compare the effects of incentives in a combined manner, and in particular use eco-driving with BEVs in a natural driving context, do not yet exist. Hence, the first objective of the present study was to investigate and compare the influence of different persuasive strategies on eco-driving with BEVs under naturalistic driving conditions.

1.1. Persuasive strategies to promote behavioural change According to [24], persuasion is understood as attempts to influence peoples’ behaviour and incentivise sustainable behavioural change [25]. While the combination of non-financial and financial incentives promises the greatest intervention effectiveness [26], in the past, various subtypes of persuasive strategies have proven successful. Giving individuals information about effective behaviours to achieve savings has been used in a variety of contexts including energy consumption [27,28], water usage [29] and transportation behaviour [30,31]. According to [28], successful feedback to motivate energy conservation in households should be presented in a clear and understandable way, repeated frequently and over a longer period, as well as based on actual consumption. Gamification is “the use of game design elements in non-game contexts” [32, p.2] such as awarding points, ranks or levels. Employing social comparison, [19] found that comparative feedback resulted in more energy savings than individual feedback; a possible explanation is generation of stronger intrinsic motivation [33,34]. Combining information, feedback and social support has been found to affect longterm environmental behavioural change [35]. Finally, many examples showcase the effect of monetary incentives, for example on work performance [36], energy conservation [37], electric vehicle adoption [38], and travel choices [39]. However, rewards only seem to be effective when they are closely linked to the target behaviour, are not too little and not too much, as well as providing not too many options [40].

1.3. Eco-driving: Attitudes and knowledge Drivers’ attitude towards eco-driving can be a predictor for environmental-friendly driving as well [57]. The two acceptance sub-facets – satisfaction and usefulness – from the Van der Laan acceptance scale [58] have been previously employed to investigate changes in drivers’ attitude [59]. Satisfaction has also been shown to increase in interaction with persuasive strategies [60]. Therefore, the second objective of the present study was to investigate participants’ attitude towards eco-driving depending on the experience of persuasive strategies and their impact on energy consumption. Finally, the presentation of feedback can result in behavioural reflection and awareness, and has been shown to increase knowledge regarding the impact of a certain habitual behaviour [61]. Furthermore, simply gaining driving experience with BEVs across three months, without additional feedback, led to increased eco-driving knowledge [62]. Moreover, there is evidence that eco-driving information and experience might result in higher eco-driving knowledge [59] and better driving performance [11]. Hence, the third objective of the present research was to investigate the impact of persuasive strategies and experience on drivers’ eco-driving knowledge and the impact of eco-driving knowledge on energy consumption.

1.2. Persuasive strategies in the context of eco-driving Some studies have investigated persuasive strategies in driving contexts. For example, real-time feedback about fuel usage proved fruitful in reducing average fuel consumption while driving an ICEV [16]. Feedback about speed, the average energy consumption and CO2emissions, motivated drivers to adopt an environmentally friendly driving style [41,42]. For instance, [42] used an eco-driving application interface with detailed feedback regarding the recommended gear, acceleration, braking and speed to motivate corporate car drivers’ general driving style and achieved an improvement in fuel efficiency by approximately 3% compared to the control group. However, from a traffic safety perspective, the distraction potential of real-time feedback is often discussed [43–45] and research recommends intermittent over continuous eco-driving feedback [46]. To avoid any distractions, feedback after driving (i.e. post-drive feedback) [47] will be investigated as an alternative. Gamification elements have been previously shown to reduce drivers’ energy consumption [48,49] and to increase drivers’ enjoyment with the experience of energy feedback and combing scores [14]. Kreußlein and Leonhardt [48] analysed the effect of a gamified ecodriving feedback displaying the winning or losing of stars while driving; this enabled drivers to gain significantly more range compared to a conventional range display. Comparison with other drivers resulted in reduced fuel consumption compared to individual feedback [50]. Financial rewards ($5 per saved litre) also resulted in a significant improvement in fuel efficiency of bus drivers [51]. Schall et al. [52] argued that a non-monetary reward (i.e. a voucher which was equivalent to the monetary reward) can have a higher effect than the monetary reward, especially in the long-term. In addition, the motivational effect of financial incentives has been shown to diminish when the incentive is removed [53]. Although, participants’ perceived worthiness of practicing eco-driving is higher when using environmental (saving potential of CO2 emissions) compared to financial feedback (saving

1.4. Present research We investigate the impact of three (stepwise combined) main interventions (i.e. persuasive strategies) on participants’ eco-driving behaviour, as compared to a baseline without feedback or intervention. We used (1) information in terms of post-drive feedback regarding energy consumption; (2) feedback plus gamification; and (3) feedback plus gamification plus a financial reward (see Table 2 for an overview). Eco-driving behaviour is operationalized as the average energy consumption when driving a BEV. Following [13]’s recommendations, we investigated eco-driving under real driving conditions with long-lasting interventions. We hypothesised that the presence of any intervention would predict eco driving, i.e. reduce average energy consumption when driving a BEV (H1a). We hypothesised that the effect size of the interventions would differ: (3) financial reward + gamification + feedback should lead to the strongest reduction, followed by (2) gamification + feedback, and then (1) feedback alone (H1b; Reward > Gamification > Feedback > Baseline). We also explored the removal of gamification and financial interventions as a post-intervention condition at the end of the study. We expected that either intervention gains would continue, possibly due to persistence or learning effects, or that energy consumption would return to baseline, possibly due to the removal of incentives. Therefore, we formulate the research question (Q) “How will a removal of interventions affect energy consumption?”. Regarding the positive influence of persuasive strategies on satisfaction [60] we hypothesised that the experience of persuasive interventions (i.e. driving feedback) would led to a more positive attitude 2

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Table 1 Addressed hypotheses and research question in the present study. H1 Persuasive Intervention Q1 Intervention removal H2 Eco-driving attitude H3 Eco-driving knowledge

H1a: Interventions will reduce energy consumption H1b: Effect sizes: Reward > Gamification > Feedback > Baseline Effect of removal of interventions on energy consumption H2a: Feedback intervention will increase attitude H2b: Increase in attitude will reduce energy consumption H3a: More BEV driving experience will increase knowledge H3b: Feedback intervention will increase knowledge H3c: Increase in knowledge will reduce energy consumption

towards eco-driving (H2a), and that this increase would result in lower energy consumption (H2b). In terms of knowledge aspects, we investigated self-reported ecodriving knowledge and hypothesised that it would increase with driving experience (H3a) and the reception of persuasive strategies, in particular feedback (H3b). We also hypothesised that an increase in knowledge about eco-driving would reduce energy consumption (H3c). See Table 1 for a summary of the hypotheses.

participants reported to have at least 1 km of previous BEV driving experience prior to the study; on average 310 km (SD = 1,943.0, Min = 0 km, Max = 2,0000 km). This study was carried out in accordance with the American Psychological Association Code of Ethics, as well as recommendations, regulations and consent templates of the Chemnitz University of Technology ethics commission. The protocol was approved by the Chemnitz University of Technology ethics commission. All subjects gave written informed consent.

2. Methodology

2.2. Field experiment setting

The study reported in this paper was part of a large-scale field study to develop a corporate multimodal sharing system [63], and was conducted from 2015 to 2018. The sharing system offered free access to four BEVs, eight pedelecs (electric bicycles) and public transport for employees working at Chemnitz University of Technology. Study participants were allowed to use the three means of transport only for business travel. The charging infrastructure and ICT solution was implemented as part of the project. The multimodal sharing system, along with its charging infrastructure and routing navigation application was used to analyse participants’ mobility and usage behaviour in the light of the incentives provided. A comprehensive overview of data collection can be found in [63]. Within the present paper, only driving data logged from the BEVs and questionnaire data is analysed.

We conducted the investigation as a longitudinal study, starting data collection on 1st October 2016 and ending on 20th July 2018. Study participants were continuously enrolled in the study throughout the data collection period, meaning participants’ first drive could happen in any condition, and subsequent drives could also occur in any of the following conditions. The distribution of first time driver was as follows: Baseline: 47, Feedback: 21, Gamification: 10, Reward: 6, Gamification II: 6, Post-Intervention: 3. All participants received a comprehensive technical briefing about the sharing system before their first usage, including vehicle and booking tool handling, conditions of use (e.g., restricted to business travel), and which data was being collected. The five experimental conditions occurred in a set order and were defined as follows: baseline without any feedback or intervention (henceforth named as Baseline), intervention with feedback regarding energy consumption (henceforth named Feedback), intervention with feedback and gamification (henceforth named Gamification), and intervention with feedback, gamification and financial reward (henceforth named Reward). At the end of the study, a post-intervention condition was conducted were any feedback and intervention were removed (henceforth named Post-intervention). Table 2 represents the data collection timeline in detail, as well as the naming convention adopted. Baseline and Post-intervention lasted five months, each intervention condition lasted three months. For each condition, the relevant information was presented inside the booking application after each completed trip (post-drive feedback). Participants had unlimited access to the presented information and could retrieve it from the project homepage via mobile device, charging station or computer. Feedback regarding energy consumption consisted of two metrics: the average energy consumption in kWh/100 km since the first participation day; and of the last BEV trip undertaken. Metrics were calculated and updated after participants had finished their trip and returned the BEV to a charging station. Participants were informed

2.1. Participants University staff received an invitation to the online application system via newsletter, press release and study homepage. Upon sign-up, participants were required to fulfil the following inclusion criteria: (1) have an active employment with Chemnitz University of Technology because of aspects of insurance law (e.g., no parental leave), (2) take part in the data collection (online questionnaire and driving data), and (3) accept the legal conditions of use (e.g. only business trips). Out of 284 applicants, 108 participants fulfilled the criteria, had at least one trip with the BEV and/or two completed points of data collection in questionnaire data. The sample consisted of 28 women and 80 men ranging from 18 to 64 years (M = 34 years, SD = 8.9). 69.4% of the participants were university educated, 21.3% were technical or administrative personnel and 9.3% were students with employment contracts. Participants reported in the application questionnaire an average of 15 years driver licence ownership (SD = 8.6, Min = 1 year, Max = 46 years), and 664 km driving distance per month with a personal car (SD = 701.2, Min = 0 km, Max = 4,000 km). 43% of the

Table 2 Experimental conditions and timeline used in the present study. Condition

Timeline

Baseline (control) Feedback (energy consumption information) Gamification (information + gamification) Reward (information + gamification + financial reward) Gamification II (information + gamification) Post-Intervention (control)

October 2016 to February 2017 March 2017 to May 2017 June 2017 to August 2017 September 2017 to November 2017 December 2017 to February 2018 March 2018 to July 2018

3

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Fig. 1. Presented eco-driving information in the experimental conditions. Feedback regarding energy consumption on the left (only in green and diagonally hatched) was presented in the first intervention condition (Feedback). Gamification, Gamification II and Reward consist of the full information. In Reward-condition participants could receive a financial reward reaching rank 1, 2 or 3.

that the presented energy consumption included only the energy used to drive, and that it was directly related to their driving style (in contrast to the average energy consumption displayed in the car, which includes all energy consumed in the vehicle such as air conditioning and heating). In the Gamification condition, feedback was combined with gamification elements. Participants were shown an anonymised user ranking, presenting the energy consumption of the 10 most ecofriendly drivers. The more energy-efficient the driving, the higher the participant's position in the ranking (position 1 being the most energyfriendly driver with the least average energy consumption). Further, each user could see his or her energy rank regardless of how low the position, as well as the average energy consumption of the whole community. The aim was to incentivise participants by increasing both self-improvement motivation as well as competitive motivation. Fig. 1 displays the feedback regarding energy consumption and gamification elements. In the Reward condition, participants were informed about the possibility of a reward in addition to the energy consumption related information and the gamification element. The top ranked driver would receive €100, the second ranked €50 and the third ranked driver €30 at the end of the phase (i.e. November 2017, see Table 2). In each intervention condition, participants received only information about their previous average energy consumption. We assessed eco-driving scales at three points of data collection via an online questionnaire. Participants received the first questionnaire after six months of study participation (T1), therefore these data points are distributed across conditions 1–3 (Baseline to Gamification). Independent of the previous duration of study participation, participants filled out the second online questionnaire in December 2017 (T2), at the end of the Reward – condition. Because of continuously enrolling study participants, the interval between T1 and T2 was at least two months, but not more than 16 months. The third point of data collection (T3) was six months later in June 2018, corresponding to the end of the Post-Intervention – condition, as well as the end of the entire study. As participants were able to continuously enrol in the study, this resulted in different sample sizes at each point of data collection. Not

every participant took part in each condition. 2.3. Battery electric vehicles and driving data Participants had access to four Smart Fortwo electric drive two seaters with an average driving range of up to 160 km (as stated in the user manual; [64]) depending on driving style and weather conditions. The BEVs were equipped with data loggers and recorded energy consumption (differentiated between board supply and traction) in kWh/ 100 km and other car related measurements at a frequency of 5 Hz during each trip. Driving data was checked for inaccurate values outside the possible range (e.g. velocity of 200 km/h), which were removed and data aggregated based on trip level. Driving sections with a velocity of 0 km/h (e.g. when stopping at the red traffic light) were excluded from analysis. The length of each trip was calculated, in km and minutes. Because of errors in energy consumption reports from on-board-diagnostics for short trips, we removed all trips of a distance < 3km and duration < 3 minutes. As a measure of driving experience for participants in the study, we added variables for the number of previously conducted drives, a cumulative sum of previously driven km, and each participant's first trip was marked as such. None of these measures by themselves predicted a change in eco-driving (all p-values > .390). For the energy consumption, we aggregated the average energy consumption in kWh/100 km for traction current for each drive, excluding power consumption for the board supply (e.g. heating, air condition, light). 2.4. Scales Eco-driving scales were assessed at three points of data collection (T1, T2, T3) with an online questionnaire. 2.4.1. Attitude towards eco-driving To assess participants’ attitude towards eco-driving, the Van der 4

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Laan acceptance scale [58] with the two sub-facets usefulness (five items) and satisfaction (four items), was used. Items were answered on a five-point bipolar continuum scale from +2 to –2. Scale reliability coefficients ranged from good to excellent (usefulness: αT1 = .90, αT2 = .92, αT3 = .88; satisfaction: αT1 = .87, αT2 = .91, αT3 = .91). For analyses, item scores were averaged and then transformed to fit a 1–5 scale.

Table 3 Reductions in average energy consumption in kwh/100 km compared to Baseline

2.4.2. Eco-driving knowledge Self-reported eco-driving knowledge was assessed on a five-item scale. Two items were adapted from Franke´s subjective range competence [65]. All items were answered on a six-point Likert scale from 1 (strongly disagree) to 6 (strongly agree). An item example is “I know exactly what I would have to do to drive the electric vehicle as energyefficiently (range-saving) as possible.”. Scale reliability coefficients were good (αT1 = .85, αT2 = .82, αT3 = .86). For analyses, item scores were averaged.

Condition

Mean

SE

t

p

Baseline (Intercept) Feedback Gamification Reward Gamification II Post-Intervention Number of previous drives Total km of BEV driving experience Trip length in km

37.00 –1.02 –2.99 –2.17 –2.27 –2.97 .02 –.05 –.42

.77 .84 .88 .97 .96 1.12 .06 .03 .03

48.05 –1.21 –3.40 –2.23 –2.36 –2.63 –.78 –.72 –12.81

<.001 .225 <.001 .026 .018 .008 .432 .469 <.001

Note: N = 93. Mean are the objective reductions in the average energy consumption between Baseline and every tested condition and control variable. t represents the effect size of the reduction effect.

The effect of the gamification condition was slightly larger than that of the financial reward, though not significantly so. Therefore H1b, that stacking interventions additively would increase effect size, was rejected. Our exploration (Q) into the removal of interventions suggests a persistence effect of eco-driving, as energy consumption in PostIntervention was significantly lower than before interventions (p = .008) and not different from Reward and both Gamification conditions. Regarding eco-driving attitudes, we hypothesised feedback intervention would increase attitude (H2a) and increase in attitude would reduce energy consumption (H2b), we found no significant effects: selfreported attitudes were not affected by any intervention condition (all p-values > .128; see Table A1 in the Appendix), nor did attitudes affect consumption in driving (all p-values > .722; see Table A2 in the Appendix). Finally, for H3a (BEV experience will increase knowledge), we found that a person's past driven kilometres with BEVs indeed predicted their perceived knowledge about eco-driving (while controlling for intervention conditions), with t = 2.27, p = .024. For H3b (Feedback intervention will increase knowledge), we tested the effect of the interventions, particularly the feedback intervention, while controlling for driving experience, and found that of the interventions, only Gamification (t = 2.17, p = .032), but not Feedback (t = .10, p = .341) nor Reward condition (t = 1.57, p = .118) predicted self-reported knowledge significantly different from Baseline (see Table A1 in the Appendix). Knowledge was also found to be significantly higher than baseline for the post-intervention survey (t = 2.33, p = .021). H3c had to be rejected, as we did not find any significant effect of self-reported eco-driving knowledge on consumption (controlling for intervention conditions and driving experience, t = .12, p = .903; see Table A2 in the Appendix).

2.5. Statistical Analyses For all analyses, we used R [66] and linear mixed-effects models with lme4 [67]. Our main hypothesis involved a one-factorial design with six levels. The six different experimental conditions were used as within-subject design, indicating that the influence of the different interventions was tested successively (Baseline, Feedback, Gamification, Reward, Gamification II and Post-Intervention). We contrasted each intervention effect against the baseline to predict lower energy consumption, so dummy coded treatment contrasts were employed, sum contrasts (stats package) were used to test effects of interventions against each other. For multiple comparisons Tukey-corrected contrasts were used. In all tests, we controlled for repeated measures by adding a random intercept for each subject. We controlled for the length of each trip, as this has been shown to affect consumption in previous studies [68,69]. We also controlled for driving experience via measures of driving experience, i.e. the number of previously completed trips and total sum of km driven so far in the study. For predictions related to attitudes and knowledge, we used linear mixed effects models, always controlling for intervention phases in which they were measured when predicting consumption. Univariate outliers were tested according to [70]; six outliers (<.1% of the data) were identified and removed (z-value > 3.05). Power calculations were conducted using powerSim of the simr package. 3. Results We recorded a total of 1,775 trips (Baseline: 270; Feedback: 311, Gamification: 300, Reward: 239, Gamification II: 379, Post-Intervention: 276). On average 19.09 trips per participant were taken within the study (SD = 27.18, Min = 1, Max = 200). The average trip was 6.05 km long (SD = 7.08, Min = 3, Max = 87) and took 13.9 minutes (SD = 10.46, Min = 3, Max = 116). In sum, participants drove 10,741 km (Min = 3, Max = 953). Testing H1a (Interventions will reduce energy consumption), our intervention related linear mixed model was significantly different from a null model (p < .001). A post-hoc power calculation showed an adequate power of 83.3% (CI 80.8–85.6). The estimated fixed effects are displayed in Table 3. The average energy consumption in Baseline condition was 37 kwh/100 km, with significant reductions from the Gamification and Reward interventions. Thus, H1a is only partly supported. Experience in the form of number of previous drives and total km driven with a BEV did not predict consumption rates. The length of trip did predict consumption rate, in the sense that the longer a trip, the more efficient the driving. Sum contrasts of each condition contrasted with the previous one revealed a significant difference between Gamification and Feedback condition, t == –3.23, p = .001. No other contrasts were significant.

4. Discussion 4.1. Summary of results Within the present research we investigated the influence of three (additive) persuasive strategies on energy consumption while driving a BEV in an organizational setting, and their influence on participants’ attitude towards and knowledge about eco-driving. We also measured the effects of attitudes and knowledge on consumption. For our sample, gamification elements and financial rewards were useful strategies to reduce the average energy consumption, even when controlling for experience with driving within the study. Feedback alone revealed no potential to motivate driving behaviour change. The largest decrease in average energy consumption was achieved by adding gamification elements. We also found persistence effects after removal of interventions. 5

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Our participants’ attitudes towards eco-driving were not affected by persuasive interventions and did not affect eco driving (i.e. energy consumption). However, both experience and the gamification intervention increased self-reported eco-driving knowledge. Reported knowledge however, did not affect consumption.

previously examined for non-BEV drivers after a test drive [59] and after three months of BEV driving experience [62]. Gamification might be a better strategy as it increases visibility of the feedback, adds an emotional and social component through competition aspects, and, increases evaluability by providing a frame of reference. With regards to the lack of impact on consumption, previous literature has looked at the gap from the perspective of intentionality to drive in an energy friendly way [57]. Possibly knowledge represents an influencing factor which only unfolds its potential once a tipping point in motivation is reached, for instance in a critical range situation where reaching the destination becomes paramount. In previous research, we found that experienced and trained unexperienced BEV drivers reported a higher level of eco-driving knowledge and an enhanced eco-driving behaviour in a critical range situation [60]. Furthermore, we only assed self-reported eco-driving knowledge, where the difference between subjective and objective knowledge could be measured with a knowledge test. This could help account for the variance from social demand in the increase of reported knowledge in the post-intervention condition.

4.2. Implications This is one of the first attempts to compare the effectiveness of different social incentives and additional financial rewards, and to examine how they affect energy savings in the context of corporate ecodriving. In our data, we found social or normative comparisons to be at least as effective, if not more so, than financial rewards, in the sense that adding financial rewards did not seem to affect energy consumption patterns in any additional positive manner. Moreover, the use of gamification elements has the benefit of being more economical, with longer-term application potential, while achieving a comparable saving potential. It was surprising that financial incentives did not affect motivation further, because studies have found that it does work (e.g. [51]). Multiple explanations can be provided, the simplest that the incentive was too little, or not task-specific enough. However, it could also be possible that additional gains from the money if it had been presented alone, through the mechanism of extrinsic motivation, were reduced again through its mix with gamification, where money has previously shown to reduce intrinsic motivation. Intrinsic motivation theory (e.g. Self-Determination Theory; [71]) could explain why financial rewards might reduce the effectiveness of gamification [40] and previous research has indeed indicated that an increasing extrinsic motivation because of extrinsic (i.e. financial) rewards can ‛crowd out’ previous intrinsic motivation [56]. However, once again one must caution that such interpretations are purely explorative; further research should look at the effects of gamification, financial and both together, while measuring intrinsic and extrinsic motivation, to be able to tease apart individual effects. Gamification might be the most appropriate strategy and has strong potential to become a standard integration in automotive HMI-design. If our findings on consumption patterns are reflective of real energy savings, many BEV stakeholders can benefit from this, including energy providers, BEV sharing providers, freight forwarders, bus companies and of course private drivers. We observed a lower energy consumption in the Post-Intervention treatment group; this effect could be because of longer-term participating drivers incorporating eco-driving techniques into their habitual driving styles after the experience of eco-driving persuasion (Gamification, Reward and Gamification II). However, this finding is explorative, and the hypothesis needs to be further investigated, as there is no way to distinguish clearly the underlying cause of the lowered energy consumption. In contrast to previous literature [60] we found no influence of persuasive strategies on participants’ attitudes towards eco-driving, nor that attitudes predict energy consumption directly. However, the weak correlation between attitudes and actual behaviour is a well-known problem, and often discussed in the context of environmental behaviour [72]. Methodological differences, i.e. subjective reporting versus objective measures of behaviour, might also explain discrepancies. Future research could be more successful if it focuses on correlates of environmentally relevant behaviour, such as motivational aspects, hedonic values [73], pro-environmental orientation [74], or pursuit of saving time [55] and comfort [75] could strengthened the link between attitude and behaviour [76] and should be considered in future research. Finally, self-reported eco-driving knowledge increased with driving experience and the experience of gamification elements, but did not predict energy consumption. The importance of practical experience for knowledge acquisition is in line with existing research and was

4.3. Limitations, strengths and further research Because our sample consisted of individuals from similar professional and social backgrounds, and because of ethical concerns about individuals being randomly assigned to reward groups - which might have upset participants - it was not possible to implement a randomised controlled trial. All participants had to be in the same condition, as we could not prevent them from telling each other about their rankings. Thus, unfortunately the implementation of a control group who do not receive any persuasive strategies, but drive a BEV for the same amount of time as the other participants, was also not possible. Regarding our stepwise and combined design for persuasive strategies, it was chosen as to our best knowledge, interactions between them have to always be considered in a realistic setting; gamification interventions that use social comparison automatically include feedback, and rewards or prizes are usually given based on achievements as compared with other participants. It might be possible to forcibly tease these concepts apart, but it can also be argued that incremental increases in their effectiveness can at least be directly attributed to the main effect of the added intervention plus its interaction with the previous interventions. Including participants in a successive manner adds noise to the data. The weather, passing of time, different routes (i.e. urban and rural) and random events that might have occurred over the course of the study cannot be discounted as possible sources of found effects, and are very difficult to control for statistically. These limitations have to be considered when interpreting results from naturalistic field trials with strong external validity. Our results should be considered with this limitation in mind. All participants were university staff, well-educated, as well as interested in new technologies. Possibly, our participants were more interested in figuring out an electric vehicle and its driving dynamic as well as the used persuasive strategies and their interference by driving than a sample randomly selected from the general population might have been. Again, care should be taken when extrapolating from our results to make broader claims. We investigated driving trips for business travel only, where charging was free of charge for users, and the vehicles were shared. Hence, participants had no initial personal motivation to practice eco-driving to save their own money. This speaks to the strength of the incentive interventions in achieving a change in behaviour, even in a situation where, except for the Reward condition, it did not provide participants any personal gain. Habituation as a driver of energy efficient behaviour is still an important factor to consider; however, as a main explanation for our intervention-related data, it can be largely discounted because not all participants started at Baseline and then proceeded through the 6

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different reward schemes at an equal pace; some participants started in the Gamification condition, for example, and then had most of their drives in the Reward condition, others started in the Feedback condition and never participated in any other the others. We controlled for experience by recording kilometres previously driven in the study for each data collection point and recording both number of previous participations as well as the condition in which participants drove for the first time. If habituation was the main explanatory variable for the reduction of consumption in the gamification and reward conditions, then we would have found driving experience to be a strong predictor for consumption reduction, and when controlled for, it would have reduced the effects found; this turned out not to be the case. However, again, for a clearer causal picture of the effects that were found, a direct between-group randomised controlled trial will be necessary. We did not assessed users’ general willingness to change their driving behaviour (their stage in a model of behavioural change, e.g. [77,78]) and presented persuasive strategies to all users equally (“onefits-all-approach”). As behavioural change is defined by a sequence of different stages and goals each stage, different strategies are effective to activate these goals and enable a behavioural change [79]. Future research should address users’ general willingness to change driving behaviour and the relationship between different persuasive strategies and change stages. Results of carry-over effects of our interventions into the PostIntervention phase are purely exploratory and no clear interpretation is possible based on the data we obtained. However, if the effect holds in replications and habituation, it might be advisable to achieve an early implementation of persuasive strategies for learning eco-driving for example at the beginning of driving school, with the aim to ensure energy-efficient use of BEVs. As the potential, but not fully realised sustainability of BEVs is often mentioned as a constraint in adoption discussions [80], it would be a strong argument to start intervening in

making BEVs “greener” in order to foster further adoption. 4.4. Conclusion With the present study, we expanded the research on persuasion and incentive strategies. We added to studies by [48,49] supporting the idea that gamification can be strongly relevant for energy saving behaviour. We also extended previous literature on financial incentives [51,52], with novel evidence that including financial rewards did not add beyond the effects of non-reward driven gamification that were already present. We provide driving data from a highly naturalistic field trial within a car sharing use case, improving on previous studies restricted to laboratory settings or instructed driving, by creating an environment that let participants choose their own times and routes to drive. Funding sources The study was funded by the European Union (Europäischer Sozialfond; ESF) and the Free State of Saxony, Germany (grant number 100231948). Any views expressed herein are those of the authors and do not necessarily reflect those of the funding bodies or partners involved in the project. Declaration of Competing Interest None. Acknowledgments The authors wish to gratefully acknowledge the editors of this issue and three anonymous reviewers for suggestions that improved the manuscript. We further wish to thank all study participants for their contribution.

Appendix Tables A1 and A2

Table A1 Influence of persuasive strategies on attitudes towards eco-driving (satisfaction with, and usefulness of eco-driving), as well as eco-driving knowledge. Condition Baseline (Intercept) Feedback Gamification Reward Post-Intervention Total km of BEV driving experience

Satisfaction Mean SE

t

p

Usefulness Mean SE

t

p

3.93 –.02 –.10 .10 –.04

30.86 –.10 –.60 .79 –.34

<.001 .921 .547 .430 .731

3.88 .01 –.01 .17 .04

34.44 .69 –.63 1.53 .34

<.001 .491 .530 .128 .745

.13 .16 .17 .13 .13

.11 .14 .14 .11 .11

Knowledge Mean SE

t

p

3.63 .16 .37 .21 .31 .00

27.34 .10 2.17 1.57 2.33 2.27

<.001 .341 .032 .118 .021 .024

.13 .16 .17 .13 .13 .00

Note: N = 108. Mean are the objective influence on the average scale value depending on each condition. t represents the effect size of the effect. Transformed satisfaction and usefulness scale ranged from 1 to 5, knowledge scale ranged from 1 to 6. Table A2 Influence of attitudes towards eco-driving (satisfaction with, and usefulness of eco-driving), as well as eco-driving knowledge on the average energy consumption in kwh/100 km when driving a BEV.

Kwh (Intercept) Scale Influence

Satisfaction Mean

SE

t

p

Usefulness Mean

SE

t

p

Knowledge Mean

SE

t

p

31.29 .32

3.64 .91

8.60 .34

<.001 .722

32.08 .12

3.89 .97

8.24 .13

<.001 .898

33.81 .10

3.46 .78

9.77 .12

<.001 .903

Note: N = 69. Mean are the objective influence of satisfaction, usefulness and knowledge on the average energy consumption. t represents the effect size of the effect.

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